EP3686781C0 - Lernverfahren und lernvorrichtung für einen objektdetektor auf der basis von cnn unter verwendung von bildverknüpfung sowie testverfahren und testvorrichtung damit - Google Patents
Lernverfahren und lernvorrichtung für einen objektdetektor auf der basis von cnn unter verwendung von bildverknüpfung sowie testverfahren und testvorrichtung damitInfo
- Publication number
- EP3686781C0 EP3686781C0 EP19219842.2A EP19219842A EP3686781C0 EP 3686781 C0 EP3686781 C0 EP 3686781C0 EP 19219842 A EP19219842 A EP 19219842A EP 3686781 C0 EP3686781 C0 EP 3686781C0
- Authority
- EP
- European Patent Office
- Prior art keywords
- learning
- test
- cnn
- object detector
- detector based
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F18/2148—Generating training patterns; Bootstrap methods, e.g. bagging or boosting characterised by the process organisation or structure, e.g. boosting cascade
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/09—Supervised learning
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/40—Image enhancement or restoration using histogram techniques
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
- G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
- G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
- G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
- G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/07—Target detection
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Multimedia (AREA)
- Molecular Biology (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Databases & Information Systems (AREA)
- Medical Informatics (AREA)
- Biodiversity & Conservation Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Image Analysis (AREA)
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US16/254,279 US10387752B1 (en) | 2019-01-22 | 2019-01-22 | Learning method and learning device for object detector with hardware optimization based on CNN for detection at distance or military purpose using image concatenation, and testing method and testing device using the same |
Publications (3)
| Publication Number | Publication Date |
|---|---|
| EP3686781A1 EP3686781A1 (de) | 2020-07-29 |
| EP3686781B1 EP3686781B1 (de) | 2024-11-06 |
| EP3686781C0 true EP3686781C0 (de) | 2024-11-06 |
Family
ID=67620645
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| EP19219842.2A Active EP3686781B1 (de) | 2019-01-22 | 2019-12-27 | Lernverfahren und lernvorrichtung für einen objektdetektor auf der basis von cnn unter verwendung von bildverknüpfung sowie testverfahren und testvorrichtung damit |
Country Status (5)
| Country | Link |
|---|---|
| US (1) | US10387752B1 (de) |
| EP (1) | EP3686781B1 (de) |
| JP (1) | JP6846069B2 (de) |
| KR (1) | KR102337367B1 (de) |
| CN (1) | CN111460877B (de) |
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| CN110770760B (zh) | 2017-05-19 | 2024-01-12 | 渊慧科技有限公司 | 视觉交互网络系统及其方法、训练方法和计算机存储介质 |
| US10572770B2 (en) * | 2018-06-15 | 2020-02-25 | Intel Corporation | Tangent convolution for 3D data |
| US10915793B2 (en) * | 2018-11-08 | 2021-02-09 | Huawei Technologies Co., Ltd. | Method and system for converting point cloud data for use with 2D convolutional neural networks |
| US12346818B2 (en) * | 2019-08-19 | 2025-07-01 | Board Of Trustees Of Michigan State University | Systems and methods for implementing flexible, input-adaptive deep learning neural networks |
| CN114693532B (zh) * | 2020-12-28 | 2025-10-03 | 富泰华工业(深圳)有限公司 | 图像校正方法及相关设备 |
| KR102637342B1 (ko) | 2021-03-17 | 2024-02-16 | 삼성전자주식회사 | 대상 객체를 추적하는 방법과 장치 및 전자 장치 |
| CN113034456B (zh) * | 2021-03-18 | 2023-07-28 | 北京百度网讯科技有限公司 | 螺栓松动的检测方法、装置、设备以及存储介质 |
| KR102618066B1 (ko) | 2023-07-11 | 2023-12-27 | 같다커뮤니케이션 주식회사 | 군인 기반 커뮤니티 애플리케이션에서 자연어 처리 및 이미지 대조를 기반으로 하여 군사 보안을 강화하는 방법, 장치 및 시스템 |
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| US9536293B2 (en) * | 2014-07-30 | 2017-01-03 | Adobe Systems Incorporated | Image assessment using deep convolutional neural networks |
| CN106156807B (zh) * | 2015-04-02 | 2020-06-02 | 华中科技大学 | 卷积神经网络模型的训练方法及装置 |
| WO2016165060A1 (en) * | 2015-04-14 | 2016-10-20 | Intel Corporation | Skin detection based on online discriminative modeling |
| US9965719B2 (en) * | 2015-11-04 | 2018-05-08 | Nec Corporation | Subcategory-aware convolutional neural networks for object detection |
| US9881234B2 (en) * | 2015-11-25 | 2018-01-30 | Baidu Usa Llc. | Systems and methods for end-to-end object detection |
| JP2018005506A (ja) * | 2016-06-30 | 2018-01-11 | 株式会社東芝 | 画像認識手法評価装置、画像認識手法評価方法、及びプログラム |
| US20180039853A1 (en) * | 2016-08-02 | 2018-02-08 | Mitsubishi Electric Research Laboratories, Inc. | Object Detection System and Object Detection Method |
| US10354159B2 (en) * | 2016-09-06 | 2019-07-16 | Carnegie Mellon University | Methods and software for detecting objects in an image using a contextual multiscale fast region-based convolutional neural network |
| US10354362B2 (en) * | 2016-09-08 | 2019-07-16 | Carnegie Mellon University | Methods and software for detecting objects in images using a multiscale fast region-based convolutional neural network |
| US11308350B2 (en) * | 2016-11-07 | 2022-04-19 | Qualcomm Incorporated | Deep cross-correlation learning for object tracking |
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| US10380741B2 (en) * | 2016-12-07 | 2019-08-13 | Samsung Electronics Co., Ltd | System and method for a deep learning machine for object detection |
| US10262237B2 (en) * | 2016-12-08 | 2019-04-16 | Intel Corporation | Technologies for improved object detection accuracy with multi-scale representation and training |
| CN108303748A (zh) * | 2017-01-12 | 2018-07-20 | 同方威视技术股份有限公司 | 检查设备和检测行李物品中的枪支的方法 |
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| KR101880901B1 (ko) * | 2017-08-09 | 2018-07-23 | 펜타시큐리티시스템 주식회사 | 기계 학습 방법 및 장치 |
| JP6972756B2 (ja) * | 2017-08-10 | 2021-11-24 | 富士通株式会社 | 制御プログラム、制御方法、及び情報処理装置 |
| JP6972757B2 (ja) * | 2017-08-10 | 2021-11-24 | 富士通株式会社 | 制御プログラム、制御方法、及び情報処理装置 |
| US10679351B2 (en) * | 2017-08-18 | 2020-06-09 | Samsung Electronics Co., Ltd. | System and method for semantic segmentation of images |
| CN107492099B (zh) * | 2017-08-28 | 2021-08-20 | 京东方科技集团股份有限公司 | 医学图像分析方法、医学图像分析系统以及存储介质 |
| US9984325B1 (en) * | 2017-10-04 | 2018-05-29 | StradVision, Inc. | Learning method and learning device for improving performance of CNN by using feature upsampling networks, and testing method and testing device using the same |
| US10169679B1 (en) * | 2017-10-13 | 2019-01-01 | StradVision, Inc. | Learning method and learning device for adjusting parameters of CNN by using loss augmentation and testing method and testing device using the same |
| US10007865B1 (en) * | 2017-10-16 | 2018-06-26 | StradVision, Inc. | Learning method and learning device for adjusting parameters of CNN by using multi-scale feature maps and testing method and testing device using the same |
-
2019
- 2019-01-22 US US16/254,279 patent/US10387752B1/en active Active
- 2019-09-27 KR KR1020190119531A patent/KR102337367B1/ko active Active
- 2019-12-16 CN CN201911295611.2A patent/CN111460877B/zh active Active
- 2019-12-27 EP EP19219842.2A patent/EP3686781B1/de active Active
-
2020
- 2020-01-09 JP JP2020002303A patent/JP6846069B2/ja active Active
Also Published As
| Publication number | Publication date |
|---|---|
| KR102337367B1 (ko) | 2021-12-10 |
| JP2020119540A (ja) | 2020-08-06 |
| CN111460877A (zh) | 2020-07-28 |
| EP3686781B1 (de) | 2024-11-06 |
| EP3686781A1 (de) | 2020-07-29 |
| KR20200091324A (ko) | 2020-07-30 |
| US10387752B1 (en) | 2019-08-20 |
| JP6846069B2 (ja) | 2021-03-24 |
| CN111460877B (zh) | 2023-10-13 |
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